@article{Liu2026, 
author = {Chao Liu and Yicheng Wang and Xueyou Li and Yuhua Qi and Binbin Li and Fan Yang},
title = {A V-SLAM localization approach based on point-line feature for GNSS-denied environment in shield tunnels},
year = {2026},
journal = {Journal of Intelligent Construction},
volume = {4},
number = {3},
pages = {9180124},
keywords = {shield tunnel, V-SLAM, GNSS-denied environment, intelligent construction},
url = {https://www.sciopen.com/article/10.26599/JIC.2026.9180124},
doi = {10.26599/JIC.2026.9180124},
abstract = {The absence of global navigation satellite system (GNSS) signals in shield tunnels presents a challenge for robotic automation. Visual simultaneous localization and mapping (V-SLAM) is widely used for robot localization in GNSS-denied scenarios, but the uniform geometry of shield tunnels hinders visual feature extraction. This study proposes a point-line feature fusion V-SLAM method that combines oriented FAST and rotated BRIEF (ORB) feature points with line segments detected by the line segment detector (LSD) algorithm and matched using the line band descriptor (LBD) algorithm. These features are integrated into a map for autonomous localization. Data from a metro shield tunnel project validate the effectiveness of this method. The results show that compared with the point feature approach, most metrics improved by 30.00%, with a maximum improvement of 49.39%. The optimization also enhanced the robustness against image degradation and improved the performance as the mileage increased, with some cases showing reduced error accumulation over longer distances.}
}